Визуализация данных на Python¶
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import numpy as np
import pandas as pd
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In [137]:
data = pd.read_csv('populations.txt', sep='\t')
data.head()
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Библиотеки визуализации данных¶
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import matplotlib
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
%matplotlib inline
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fig, ax1 = plt.subplots(1, 1)
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fig, ax = plt.subplots(2, 2, figsize=(12,10))
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from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
def offset_off(x):
x.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))
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fig, ax = plt.subplots(1,1)
ax.plot(data['year'], data['hare'])
offset_off(ax)
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fig, ax = plt.subplots(2, 2, figsize = (12,8))
ax[0][0].plot(data['year'], data['hare'], color ='#8c92ac')
offset_off(ax[0][0])
ax[0][1].plot(data['year'], data['lynx'], color = '#ffa500')
offset_off(ax[0][1])
ax[1][0].plot(data['year'], data['carrot'], color = '#b06500')
offset_off(ax[1][0])
ax[1][1].plot(data['year'], data['hare'], label = 'Hares', color='#8c92ac', ls = ':');
ax[1][1].plot(data['year'], data['lynx'], label = 'Lynxes', color='#ffa500', ls = '--');
ax[1][1].plot(data['year'], data['carrot'], label = 'Carrots', color='#b06500', ls = '-');
offset_off(ax[1][1])
Упражнение¶
Реализовать отображение графика с четырьмя подокнами. На первых трех необходимо отобразить по отдельности популяции зайцев, рысей, моркови, и на четвертом отобразить их всех вместе.
Проработать внешний вид графиков - data-ink ratio, согласованность, ясность - что есть что - , целостность и тд. Например, убедиться, что про каждый график известно, к чему он относится - зайцам, рысям или моркови. А так же убедиться, что для одних и тех же объектов используются одни и те же цвета.
Решение¶
In [13]:
fig, ax1 = plt.subplots(2, 2, figsize=(12,8))
ax1[0][0].set_xlabel('Time', fontsize = 10)
ax1[0][0].set_ylabel('Hares', fontsize = 10)
ax1[0][0].plot(data['year'], data['hare'], color='#8c92ac', ls = ':');
ax1[0][0].spines['right'].set_visible(False)
ax1[0][0].spines['top'].set_visible(False)
ax1[0][0].yaxis.set_ticks_position('left')
ax1[0][0].xaxis.set_ticks_position('bottom')
for axis in ['top','bottom','left','right']:
ax1[0][0].spines[axis].set_linewidth(0.5)
#Lynxes
ax1[0][1].set_xlabel('Time', fontsize = 10)
ax1[0][1].set_ylabel('Lynxes', fontsize = 10)
ax1[0][1].plot(data['year'], data['lynx'], color='#b06500', ls = '-');
ax1[0][1].spines['right'].set_visible(False)
ax1[0][1].spines['top'].set_visible(False)
ax1[0][1].yaxis.set_ticks_position('left')
ax1[0][1].xaxis.set_ticks_position('bottom')
for axis in ['top','bottom','left','right']:
ax1[0][1].spines[axis].set_linewidth(0.5)
#Carrots
ax1[1][0].set_xlabel('Time', fontsize = 10)
ax1[1][0].set_ylabel('Carrots', fontsize = 10)
ax1[1][0].plot(data['year'], data['carrot'], color='#ffa500', ls = '--');
ax1[1][0].spines['right'].set_visible(False)
ax1[1][0].spines['top'].set_visible(False)
ax1[1][0].yaxis.set_ticks_position('left')
ax1[1][0].xaxis.set_ticks_position('bottom')
for axis in ['top','bottom','left','right']:
ax1[1][0].spines[axis].set_linewidth(0.5)
# All of them
ax1[1][1].set_xlabel('Time', fontsize = 10)
ax1[1][1].set_ylabel('Trends in the Forest', fontsize = 10)
ax1[1][1].plot(data['year'], data['hare'], label = 'Hares', color='#8c92ac', ls = ':');
ax1[1][1].plot(data['year'], data['carrot'], label = 'Carrots', color='#ffa500', ls = '--');
ax1[1][1].plot(data['year'], data['lynx'], label = 'Lynxes', color='#b06500', ls = '-');
ax1[1][1].legend(loc=1, fontsize=10, frameon=False) # upper left corner
ax1[1][1].spines['right'].set_visible(False)
ax1[1][1].spines['top'].set_visible(False)
ax1[1][1].yaxis.set_ticks_position('left')
ax1[1][1].xaxis.set_ticks_position('bottom')
for axis in ['top','bottom','left','right']:
ax1[1][1].spines[axis].set_linewidth(0.5)
offset_off(ax1[0][0])
offset_off(ax1[0][1])
offset_off(ax1[1][1])
offset_off(ax1[1][0])
fig.tight_layout()
График можно сохранить в виде файла:¶
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fig.savefig("my_new_plot.png")
Доступные форматы, какие из них гарантируют сохранение лучшего качества?¶
Matplotlib может сгенерировать результат высокого качества в разных форматах, в т.ч. PNG, JPG, EPS, SVG, PDF. Для научных статей рекомендуем использовать PDF везде, где это возможно. (В документы LaTeX, собираемые с помощью pdflatex, PDF изображения можно включасть с помощью команды includegraphics).
EPS, PDF, SVG - векторные форматы, что означает возможность редактирования изображения в программах подобных Adobe illustrator с сохранением возможности редактирования отдельных элементов изображения - линий, точек, текста и пр.
PNG, JPG - растровые форматы, как фото. В программах редактирования изображений, например, Adobe Illustrator, обычно доступен только один объект для редактирования.
pandas¶
https://pandas.pydata.org/pandas-docs/stable/visualization.html
- ‘bar’ or ‘barh’ for bar plots
- ‘hist’ for histogram
- ‘box’ for boxplot
- ‘kde’ or 'density' for density plots
- ‘area’ for area plots
- ‘scatter’ for scatter plots
- ‘hexbin’ for hexagonal bin plots
- ‘pie’ for pie plots
Основные возможности Pandas¶
In [15]:
dates = pd.date_range('20130101', periods=6)
dates
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df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
df
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view¶
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df.head()
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df.tail()
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df.index
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df.columns
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df.describe()
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select¶
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df['A']
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df[0:3]
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df[df.A > 0]
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df[(df.A > 0) & (df.B < 0)]
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df[df > 0]
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s = pd.Series(np.nan, index=[49,48,47,46,45, 1, 2, 3, 4, 5])
s
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s.iloc[:3]
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s.loc[:3]
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set¶
In [30]:
df.at[dates[0],'A'] = 0
df
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df[df.A < 0] = 1
df
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df['E'] = 1
df
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calculate¶
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df.mean()
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df.max()
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df['A'].apply(lambda x: x + 1)
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join¶
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left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
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left
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right
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pd.merge(left, right, on='key')
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group¶
In [40]:
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
df
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In [41]:
def func(df, ind, col):
return df[col].loc[ind][0]
df.groupby(lambda x: func(df, x, 'A')).sum()
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df.groupby(['A','B']).sum()
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dir(df.groupby(['A','B']))
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pivot table¶
In [44]:
df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,
'B' : ['a', 'b', 'c'] * 4,
'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
'D' : np.random.randn(12),
'E' : np.random.randn(12)})
df
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In [45]:
pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
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plot¶
In [46]:
p = data.groupby('year').sum().plot(subplots=True, figsize=(20, 20), rot=0, sharey=True, legend=True)
offset_off(p[0])
In [47]:
data.plot.bar(x='year')
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seaborn¶
In [48]:
import seaborn as sns
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sns.barplot(x='year', y='hare', data=data, palette="BuGn_d")
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plotly¶
In [200]:
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
init_notebook_mode()
import plotly.graph_objs as go
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hares = [
go.Bar(
x=data['year'],
y=data['hare']
)
]
iplot(hares, filename='basic-bar')
bokeh¶
In [52]:
from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
from bokeh.charts import Bar
output_notebook()
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p = Bar(data, 'year', values='hare', title="Hare")
show(p)
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import pygal
In [55]:
# from IPython.display import SVG, HTML
import pygal
from IPython.display import display
bar_chart = pygal.Bar()
bar_chart.add('Hare', data['hare'].values)
bar_chart.x_labels = map(str, data['year'].values)
display({'image/svg+xml': bar_chart.render()}, raw=True)
Виды графиков¶
Scatterplot¶
In [56]:
fig, ax1= plt.subplots(1, 1)
ax1.plot(data['year'], data['hare'], 'o');
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data.plot.scatter(x='year', y='hare')
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Bar Charts (столбчатые диаграммы)¶
In [58]:
fig, ax1= plt.subplots(1, 1)
width=0.2
ax1.bar(data['year'], data['hare'], width); # параметр width изменяет ширину полосы
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data.plot.bar(x='year', y='hare')
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In [60]:
fig, ax1= plt.subplots(1, 1)
width=0.8
ax1.bar(data['year'], data['hare'], width, color='#98cff4');
ax1.bar(data['year'], data['lynx'], width, color='#ffe4e1', bottom=data['hare']); # Если указать bottom, полосы будут отрисованы над указанными
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data.plot.bar(x='year', y=['hare', 'lynx', 'carrot'], stacked=True)
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Area plots (диаграммы областей)¶
In [62]:
fig, ax1= plt.subplots(1, 1)
ax1.fill_between(data['year'], 0, data['hare'])
ax1.set_ylabel('Area between \n y=0 and hares')
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Stacked Area plots (составные диаграммы областей)¶
In [63]:
fig, ax1= plt.subplots(1, 1)
ax1.stackplot(data['year'], data['hare'], data['lynx'], data['carrot'])
ax1.legend(['hares','lynxes','carrots'], frameon=False,loc='upper center');
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data.plot.area(x='year')
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data.plot.area(x='year', stacked=False)
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Grouped bar charts (сгруппированные столбчатые диаграммы)¶
In [66]:
hares5=data['hare'][0:5]
lynxes5=data['lynx'][0:5]
new_t5=data['year'][0:5]
fig, ax1= plt.subplots(1, 1, figsize=(12,5))
bar_width=0.3
hares_bar = ax1.bar(new_t5, hares5, bar_width,
color='b',
label='Hares')
lynxes_bar = ax1.bar(new_t5 + bar_width, lynxes5, bar_width,
color='r',
label='Lynxes')
ax1.set_xlabel('Year')
ax1.set_ylabel('Population')
plt.title('Population by Species')
plt.xticks(new_t5 + bar_width, ('1900', '1901', '1902', '1903', '1904'))
plt.legend();
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data[data['year'] < 1905][['hare', 'lynx', 'year']].plot.bar(x='year', y=['hare', 'lynx'])
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Круговые диаграммы¶
In [68]:
fig = plt.figure()
ax = plt.axes([0.025,0.025,0.95,0.95], polar=True) # This is a different way to initialize the axes of a figure
N = len(data['year'])
theta = np.arange(0.0, 2*np.pi, 2*np.pi/N) #we need to map our time data to the angles in a circle
radii = data['hare']
mywidth = 0.3
bars = plt.bar(theta, radii, width=mywidth, bottom=0.0)
rmax=np.max(radii)
for r,bar in zip(radii, bars):
bar.set_facecolor( plt.cm.Pastel1_r(r/rmax)) #We are using here the colormap, which takes as input a number between 0 and 1
bar.set_alpha(0.5) # With this we set the transparency of the plot. Try to put it equal to 1
ax.set_xticks(np.pi/180*np.linspace(0, 360, N+1))
ax.set_xticklabels((data['year'].astype(int)))
ax.set_yticklabels([])
plt.show()
Distribution Plots (диаграммы распределения)¶
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mu = 100.0
sigma1 = 15.0
A1 = np.random.normal(mu, sigma1, 10000) # Let's generate fake data, like IQ measurements
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fig, ax= plt.subplots(1, 1)
# the histogram of the data
n, bins, patches = plt.hist(A1, 50, normed=1, facecolor='#368d5c', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.show()
In [71]:
fig, ax= plt.subplots(1, 1)
# the histogram of the data
n, bins, patches = plt.hist(A1, 50, normed=1, facecolor='#368d5c', alpha=0.75)
# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma1)
l = plt.plot(bins, y, 'r--', linewidth=2)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.show()
In [72]:
import seaborn as sns
p = sns.distplot(A1)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
Density plot (диаграмма плотности распределения)¶
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df = pd.DataFrame(A1)
ax = df.plot(kind='density', color = 'black')
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.legend().set_visible(False)
Comparing distributions (сравнение распределений)¶
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mu = 100.0
sigma1 = 15.0
sigma2 = 25.0
A1 = np.random.normal(mu, sigma1, 10000) # IQ measurements of humans
A2 = np.random.normal(mu, sigma2, 10000) # IQ measurements of aliens
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fig, ax= plt.subplots(1, 1)
# the histogram of the data
n, bins, patches = plt.hist(A1, 50, normed=1, facecolor='#368d5c', alpha=0.75)
n, bins, patches = plt.hist(A2, 50, normed=1, facecolor='#efbb38', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.show()
Boxplots (ящик с усами)¶
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fig, ax= plt.subplots(1, 1)
ax.boxplot([A1, A2]);
plt.setp(ax, xticklabels=['A1', 'A2']);
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fig, ax= plt.subplots(1, 1)
bp=ax.boxplot([A1, A2]);
plt.setp(ax, xticklabels=['A1', 'A2'])
plt.setp(bp['boxes'], color='black')
plt.setp(bp['whiskers'], color='black')
plt.setp(bp['fliers'], marker='o', MarkerFaceColor='red');
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mu = 100.0
sigma1 = 15.0
sigma2 = 25.0
A1 = np.random.normal(mu, sigma1, 10000) # IQ measurements of humans
A2 = np.concatenate((np.random.normal(mu-50, sigma1, 10000), np.random.normal(mu+50, sigma2, 10000)), axis=0) # IQ measurements of aliens
In [79]:
fig, ax= plt.subplots(1, 1)
ax.boxplot([A1, A2])
plt.setp(ax, xticklabels=['A1', 'A2']);
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fig, ax= plt.subplots(1, 1)
ax.violinplot([A1, A2], showmeans=False, showmedians=True);
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df=pd.read_csv('crimeRatesByState2005.tsv',header=0,sep='\t')
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df.head()
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In [83]:
state_data=df.as_matrix(columns=df.columns[1:])
state_names=np.array(df['state'])
In [84]:
fig, ax = plt.subplots(1, 1)
sc = plt.scatter(df['murder'], df['burglary'], s=df['population'] / 100000, c=df['motor_vehicle_theft'], alpha=0.5, cmap=plt.cm.get_cmap('viridis') )
plt.colorbar(sc)
# This figure is not final: you should put labels, title, units, remove the top and right axes if needed, add a grid, and so on.
# But you know how to do that!
Out[84]:
In [85]:
fig, ax= plt.subplots(1, 1, figsize=(10,5))
plt.scatter(df['murder'], df['burglary'], s=df['population']/100000, c=df['motor_vehicle_theft'], alpha=0.5, cmap=plt.cm.get_cmap('viridis'))
for i in range(len(state_names)):
ax.annotate(state_names[i], (state_data[i,0] + 0.2, state_data[i,4] + 10))
plt.colorbar();
# The result is a bit cluttered, but it is hard to untangle the text in an
# automatic way. We can solve by using intereactive visualization
In [86]:
fig, ax = plt.subplots(1,1, figsize = (10,5))
state_names=np.array(df['state'])
x = np.array(df['murder'])
y = np.array(df['burglary'])
area = np.array(df['population']) / 30000
colours = np.array(df['Robbery'])
text = np.array(df['state'])
ax.scatter(x, y, s = area, c = colours, cmap = 'inferno', alpha = 0.5, linewidth = 0)
ax.set_xlabel('Murder', fontsize = 10)
ax.set_ylabel('Bulglarly', fontsize = 10)
for i, state in enumerate(text):
ax.annotate(state, (x[i],y[i]), fontsize = 7)
In [ ]:
In [87]:
trace0 = go.Scatter(
x=x,
y=y,
mode='markers',
marker=dict(
size=area,
sizemode='area',
sizemin=4,
color=colours
),
text=text
)
data = [trace0]
iplot(data, filename='bubblechart-size-ref')
In [88]:
from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
output_notebook()
p = figure()
p.scatter(x, y, radius=area / 1000., fill_color='black', fill_alpha=0.8, line_color=None)
show(p)
Качество визуализации для перезентации¶
- хорошая визуализация
NYT
http://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html
Блог plot.ly
https://plotlyblog.tumblr.com/ - плохая визуализация
http://viz.wtf/
In [89]:
import seaborn as sns
sns.pairplot(df, kind="reg")
Out[89]:
In [90]:
df.corr()
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Heatmap (тепловая карта)¶
In [91]:
nba = pd.read_csv('nba.csv', index_col=0)
# Normalize data columns
nba_norm = (nba - nba.mean()) / (nba.max() - nba.min())
In [92]:
nba_norm.head()
Out[92]:
In [93]:
fig, ax = plt.subplots(1, 1, figsize=(15, 12))
ax.pcolor(nba_norm, cmap=plt.cm.get_cmap('Blues'), alpha=0.8)
# put the major ticks at the middle of each cell
ax.set_yticks(np.arange(nba_norm.shape[0]) + 0.5, minor=False)
ax.set_xticks(np.arange(nba_norm.shape[1]) + 0.5, minor=False)
# # want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()
# Set the labels
# label source:https://en.wikipedia.org/wiki/Basketball_statistics
labels = ['Games','Minutes','Points','Field goals made','Field goal attempts','Field goal percentage','Free throws made','Free throws attempts','Free throws percentage','Three-pointers made','Three-point attempt','Three-point percentage','Offensive rebounds','Defensive rebounds','Total rebounds','Assists','Steals','Blocks','Turnover','Personal foul'];
# note I could have used nba_sort.columns but made "labels" instead
ax.set_xticklabels(labels, minor=False)
ax.set_yticklabels(nba_norm.index, minor=False)
# rotate the
t = plt.xticks(rotation=90)
In [94]:
trace = go.Heatmap(
z=nba_norm.as_matrix(),
x=labels,
y=nba_norm.index,
colorscale='Blues',
opacity=0.8,
)
data=[trace]
iplot(data, filename='basic-heatmap')
In [ ]:
Работа с геоданными¶
In [95]:
# Import the basemap package
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
airports = pd.read_csv('https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat', header=None, dtype=str)
airports.columns = ["id", "name", "city", "country", "code", "icao", "latitude", "longitude", "altitude", "offset", "dst", "timezone", 'dat1', 'dat2']
# Create a map on which to draw. We're using a mercator projection, and showing the whole world.
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
# Draw coastlines, and the edges of the map.
m.drawcoastlines()
m.drawmapboundary()
# Convert latitude and longitude to x and y coordinates
x, y = m(list(airports["longitude"].astype(float)), list(airports["latitude"].astype(float)))
# Use matplotlib to draw the points onto the map.
m.scatter(x,y,1,marker='o',color='red')
# Show the plot.
plt.show()
In [96]:
# Make a base map with a mercator projection. Draw the coastlines.
routes = pd.read_csv('https://raw.githubusercontent.com/jpatokal/openflights/master/data/routes.dat', header=None, dtype=str)
routes.columns = ["airline", "airline_id", "source", "source_id", "dest", "dest_id", "codeshare", "stops", "equipment"]
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
m.drawcoastlines()
# Iterate through the first 3000 rows.
for name, row in routes[:3000].iterrows():
try:
# Get the source and dest airports.
source = airports[airports["id"] == row["source_id"]].iloc[0]
dest = airports[airports["id"] == row["dest_id"]].iloc[0]
# Don't draw overly long routes.
if abs(float(source["longitude"]) - float(dest["longitude"])) < 90:
# Draw a great circle between source and dest airports.
m.drawgreatcircle(float(source["longitude"]), float(source["latitude"]), float(dest["longitude"]), float(dest["latitude"]),linewidth=1,color='b')
except (ValueError, IndexError):
pass
# Show the map.
plt.show()
In [97]:
import sklearn.datasets.california_housing as ch
dataset = ch.fetch_california_housing()
X = dataset.data
Y = dataset.target
plt.figure(figsize=(10, 10))
lllon, lllat, urlon, urlat = X[:, -1].min(), X[:, -2].min(), X[:, -1].max(), X[:, -2].max()
m = Basemap(
llcrnrlon=lllon,
llcrnrlat=lllat,
urcrnrlon=urlon,
urcrnrlat=urlat,
projection='merc',
resolution='h'
)
m.drawcoastlines(linewidth=0.5)
m.drawmapboundary(fill_color='#47A4C9', zorder=1)
m.fillcontinents(color='#88D8B0',lake_color='#47A4C9', zorder=2)
parallels = np.linspace(lllat, urlat, 10)
m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
# draw meridians
meridians = np.linspace(lllon, urlon, 10)
m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
colors = [plt.cm.hot(int((y - Y.min()) / (Y.max() - Y.min()) * 256)) for y in Y]
m.scatter(X[:, -1], X[:, -2], latlon=True, zorder=3, lw=0, c=colors)
plt.annotate('San Francisco', xy=(0.04, 0.5), xycoords='axes fraction', color='white', size=15)
plt.annotate('Los Angeles', xy=(0.4, 0.08), xycoords='axes fraction', color='white', size=15)
plt.show()
In [98]:
import folium
m = folium.Map(location=[45.5236, -122.6750])
m
Out[98]:
In [99]:
import folium
# Get a basic world map.
airports_map = folium.Map(location=[30, 0], zoom_start=2)
# # Draw markers on the map.
for idx, (name, row) in enumerate(airports.iterrows()):
folium.Marker([float(row["latitude"]), float(row["longitude"])], popup=row["name"]).add_to(airports_map)
if idx > 100:
break
# Create and show the map.
airports_map
Out[99]:
In [100]:
import geoplotlib
from geoplotlib.utils import read_csv
data = read_csv('flights.csv')
geoplotlib.graph(data,
src_lat='lat_departure',
src_lon='lon_departure',
dest_lat='lat_arrival',
dest_lon='lon_arrival',
color='hot_r',
alpha=16,
linewidth=2)
geoplotlib.show()
networkx + plotly¶
https://networkx.github.io/ https://plot.ly/python/network-graphs/
In [135]:
import plotly.plotly as py
from plotly.graph_objs import *
import networkx as nx
G=nx.random_geometric_graph(200,0.125)
pos=nx.get_node_attributes(G,'pos')
dmin=1
ncenter=0
for n in pos:
x,y=pos[n]
d=(x-0.5)**2+(y-0.5)**2
if d<dmin:
ncenter=n
dmin=d
p=nx.single_source_shortest_path_length(G,ncenter)
edge_trace = Scatter(
x=[],
y=[],
line=Line(width=0.5,color='#888'),
hoverinfo='none',
mode='lines')
for edge in G.edges():
x0, y0 = G.node[edge[0]]['pos']
x1, y1 = G.node[edge[1]]['pos']
edge_trace['x'] += [x0, x1, None]
edge_trace['y'] += [y0, y1, None]
node_trace = Scatter(
x=[],
y=[],
text=[],
mode='markers',
hoverinfo='text',
marker=Marker(
showscale=True,
# colorscale options
# 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' |
# Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'
colorscale='YIGnBu',
reversescale=True,
color=[],
size=10,
colorbar=dict(
thickness=15,
title='Node Connections',
xanchor='left',
titleside='right'
),
line=dict(width=2)))
for node in G.nodes():
x, y = G.node[node]['pos']
node_trace['x'].append(x)
node_trace['y'].append(y)
for node, adjacencies in enumerate(G.adjacency()):
node_trace['marker']['color'].append(len(adjacencies))
node_info = '# of connections: '+str(len(adjacencies))
node_trace['text'].append(node_info)
fig = Figure(data=Data([edge_trace, node_trace]),
layout=Layout(
title='<br>Network graph made with Python',
titlefont=dict(size=16),
showlegend=False,
hovermode='closest',
margin=dict(b=20,l=5,r=5,t=40),
annotations=[ dict(
text="Python code: <a href='https://plot.ly/ipython-notebooks/network-graphs/'> https://plot.ly/ipython-notebooks/network-graphs/</a>",
showarrow=False,
xref="paper", yref="paper",
x=0.005, y=-0.002 ) ],
xaxis=XAxis(showgrid=False, zeroline=False, showticklabels=False),
yaxis=YAxis(showgrid=False, zeroline=False, showticklabels=False)))
iplot(fig, filename='networkx')
In [ ]: